Critical Infrastructures in the Past, Present and Future: History
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Subjects: Agricultural Engineering
Contributor:
Antonella Petrillo

Critical infrastructures are those material resources, services, information technology systems, networks and infrastructure assets that, if damaged or destroyed, would cause serious repercussions on the crucial functions of society, including the supply chain, health, security and the economic or social well-being of the state and the population. 

  • critical infrastructure
  • environmental
  • risk
  • safety

1. Critical Infrastructures

The most industrialized countries are equipped with increasingly extensive and sophisticated infrastructure systems, so-called critical infrastructures (CIs) such as energy distribution networks and transport infrastructures [1]. The term critical infrastructure is defined in Section 1016(e) of the USA Patriot Act of 2001 as those “systems and goods, both physical and virtual, so vital to the nation that their malfunctioning or destruction would produce a debilitating impact on the security of citizens, on the economic security of the nation, on national public health and on any combination of the above” [2]. Europe has also issued its own CIs protection program. In fact, in June 2004, the European Council took the initiative to call for the preparation of a strategy for the protection of CIs in the territory of the Union from possible terrorist attacks, which led the Commission to issue Communication 702 of 2004. This activity of the Commission led in 2008 to the approval of Directive 2008/114/EC which currently forms the basis of EU legislation on CIs. A CI is defined in the directive as “an element, system or part thereof located in the Member States which is essential for the maintenance of the vital functions of society, the health, safety and economic and social well-being of citizens and whose damage or destruction would have a significant impact in a Member State due to the impossibility of maintaining those functions” [3].

2. CLUSTER#1 “Risk Assessment” 

The documents belonging to this selection are related to “Risk Assessment”. Table 1 summarizes a classification of documents by year, type of publication and the main focus. Through detailed analysis of them in detail, it emerges that Rydén Sonesson et al. [4] have proposed a risk analysis across transportation, energy and telecommunication in Sweden. The document highlights the importance of using integrated tools in order to identify risks and establish strategic priorities for managing very complex and global systems. Some authors, such as Michalis and Sentenac [5], proposed very sectoral and specific studies such as, for example, an investigation of the condition of dams in Scotland using Electromagnetic (EM) sensing. It is also interesting to mention the research developed by Johnson et al. [6] in which the use of probabilistic risk analysis (PRA) for critical infrastructure is proposed. Technological innovation also makes it possible to process data capable of defining resilient models of critical infrastructure as clarified by Meslem et al. [7] who developed a customized framework/software based on the outcome of the risk and cost-benefit analysis relating to the liquefaction risk. Furthermore, Veeraraghavan et al. [8] have developed a software to monitor CIs, in this case an open-source software for seismic risk assessment. The importance of using software tools is also demonstrated by the study proposed by Donratanapat et al. [9] that develops a Python web application to assess the potential impacts of flooding on CIs. The analysis of the documents also highlights how multicriteria decision-making approaches are useful methods for managing critical infrastructures.
Table 1. Classification of documents belonging to CLUSTER#1 “Risk Assessment”.
Authors Ref. Year Type of Publication Main Focus
Rydén Sonesson et al. [4] 2021 Theoretical Risks cross-sector analysis
Michalis and Sentenac [5] 2021 Theoretical/Application Dam monitoring
Johnson et al. [6] 2021 Theoretical Probabilistic Risk Analysis
Meslem et al. [7] 2021 Software/Application Liquefaction hazard
Veeraraghavan et al. [8] 2021 Software/Application Seismic analysis
Donratanapat et al. [9] 2020 Software/Application Flood emergences/ Hurricanes
Chou and Ongkowijoyo [10] 2019 Theoretical Decision Making & Risk Management
Vamvakeridou et al. [11] 2020 Case Study Flood emergences
Turskis et al. [12] 2019 Theoretical Decision Making & Risk Management
Přibyl et al. [13] 2018 Theoretical/Application Road tunnel
Greiving et al. [14] 2021 Case Study Urban regions
Kasmi et al. [15] 2021 Theoretical/Application Risk priority analysis
Di Bona et al. [16] 2020 Theoretical/Application Nuclear power plants
Boothroyd et al. [17] 2021 Theoretical/Application River erosion
Fekete [18] 2020 Case Study Flood emergences/cascading effect
Rehak et al. [19] 2018 Theoretical Cascading effects
Esposito et al. [20] 2020 Theoretical/Application Non-nuclear infrastructures (Stress test)
Argyroudis et al. [21] 2020 Case Study Non-nuclear infrastructures (Stress test)
Huff et al. [22] 2019 Theoretical Decision Making & Risk Management
Mokhor et al. [23] 2019 Review Cybersecurity
de Bruijn et al. [24] 2019 Review Flood emergences
Karbowski et al. [25] 2019 Theoretical/Application Theoretical/Application
Murdock et al. [26] 2018 Theoretical Flood emergences
Pearson et al. [27] 2018 Review Flood emergences
Tweneboah-Koduah and Buchanan [28] 2018 Theoretical Cybersecurity
Zimmermann et al. [29] 2018 Theoretical Water infrastructure
Wang et al. [30] 2018 Theoretical SCADA systems
Mao and Li [31] 2018 Theoretical Interdependency analysis/Disturbance propagation
Klügel and Stäuble-Akcay [32] 2018 Theoretical Seismic analysis
Thacker et al. [33] 2018 Theoretical/Application Hydrometeorological risk
Thacker et al. [34] 2017 Theoretical/Application Interdependency analysis
Bloomfield et al. [35] 2017 Theoretical/Application Interdependency analysis
Delvosalle et al. [36] 2017 Theoretical/Application Interdependency analysis
Lam et al. [37] 2017 Theoretical/Application Coastal infrastructure/cyclone
Gonzalez-Granadillo et al. [38] 2017 Case Study SCADA systems
Espada et al. [39] 2017 Theoretical/Application Flood emergences
Ongkowijoyo and Doloi [40] 2017 Theoretical/Application Risk priority analysis
Daniel and Nicolae [41] 2017 Theoretical Power safety
van Staalduinen et al. [42] 2017 Theoretical Risk priority analysis

3. CLUSTER#2 “Risk Environment”

The documents belonging to this selection are related to “Risk Environment”. Table 2 summarizes a classification of documents by year, type of publication and main focus. Analyzing in detail each of them, it emerges that Imteaj et al. [43] has proposed a distributed machine learning technique called Federated Learning (FL) to predict the probable outage and resource status of CIs. Depina et al. [44] investigates the application of the Performance-Based Wind Engineering (PBWE) methodology to the risk assessment of critical telecommunication infrastructure subjected to wind hazard. An interesting concept is discussed by Hendricks et al. [45] claiming that existing environmental justice and hazard vulnerability literature inadequately addresses key texts and topics related to critical physical infrastructure, including stormwater, green space, sewerage, energy, and roads, among other systems. Yuan et al. [46] use an Internet of People (IoP) enabled framework to assess a road network’s performance loss during disasters, illustrating a case study of hurricane Florence in Wilmington (USA). Wahab et al. [47] develop a method to calculate the vulnerability of a residential building using four factors (susceptibility, surrounding environment, landslide intensity and people) for the assessment. Der Sarkissian et al. [48] evaluated the state of Saint-Martin’s CI before and after Hurricane Irma and, accordingly, reveal the indicators to assess during reconstruction projects. 
Table 2. Classification of documents belonging to CLUSTER#2 “Risk Environment”.
Authors Ref. Year Type of Publication Main Focus
Imteaj et al. [43] 2021 Theoretical Resource-limitations
Depina et al. [44] 2021 Theoretical Performance analysis
Hendricks et al. [45] 2021 Theoretical Vulnerability
Yuan et al. [46] 2021 Case Study Internet of People (IoP)
Wahab et al. [47] 2021 Theoretical/Application Vulnerability
Der Sarkissian et al. [48] 2021 Case Study Recovery
Thompson et al. [49] 2021 Theoretical/Application Long-term planning
Baggott et al. [50] 2020 Theoretical/Application Decision Making & Risk Management
Rød et al. [51] 2020 Theoretical ISO 31000
Lo et al. [52] 2020 Theoretical Decision Making & Risk Management
Benmokhtar et al. [53] 2020 Theoretical Decision Making & Risk Management
Hawchar et al. [54] 2020 Theoretical Decision Making & Risk Management
Gheorghe et al. [55] 2018 Theoretical Interdependency analysis
Serre and Heinzlef [56] 2018 Theoretical Cascading effects
Braun et al. [57] 2018 Case Study Vulnerability
Jaïdi et al. [58] 2018 Theoretical/Application Decision Making & Risk Management
Krings et al. [59] 2018 Theoretical/Application Risk management
Häyhtiö and Zaerens [60] 2017 Theoretical/Application Risk management
Capano [61] 2017 Review Risk management
Wilson et al. [62] 2017 Theoretical/Application Vulnerability (volcanic)
Flatscher et al. [63] 2017 Theoretical/Application Risk management

4. CLUSTER#3 “Human Factors” 

The documents belonging to this selection are related to “Human Factors”. Table 3 summarizes a classification of documents by year, type of publication and main focus. Analyzing in detail each of them, it emerges that this cluster presents heterogeneous monoscripts since the human factor is analyzed from different points of view. For example, Le Blanc [64] describes human factors challenges in developing cyber informed risk assessment for CIs. While, Khanam et al. [65] have provided a framework for assessing the risk factors of our modern infrastructure located in vulnerable coastal areas. A rather different perspective is analyzed in the research developed by Silver et al. [66] in which a behavioral risk factor surveillance system is discussed. Splichalova et al. [67] aimed to demonstrate the importance of the decision-making process of critical infrastructures and therefore the fundamental role of the human factor in this process. Similarly, Rehak [68] has argued on the importance of individual factors in organizational resilience of CIs. Ghafir et al. [69] and Panda et al. [70] have proposed a training framework useful for operators of CIs. Petrillo et al. have presented a hybrid model for human error probability analysis [71], called Emergency Human Error Analysis (EHEA), which considers all contingency factors that influence decisions and actions of the operator. Finally, Panteli and Mancarella [72] have discussed the relationship between the resilience of CIs and human response as a key dimension to monitor CIs. 
Table 3. Classification of documents belonging to # CLUSTER#3 “Human Factors”.
Authors Ref. Year Type of Publication Main Focus
Le Blanc [64] 2021 Theoretical/Application Risk analysis
Khanam et al. [65] 2021 Case Study Vulnerability
Silver et al. [66] 2020 Theoretical Behavioral risk
Splichalova et al. [67] 2020 Theoretical Decision making
Rehak [68] 2020 Theoretical Decision making
Ghafir et al. [69] 2018 Theoretical/Application Behavioral risk
Panda et al. [70] 2018 Theoretical/Application Behavioral risk
Petrillo et al. [71] 2017 Theoretical/Application Human error probability
Panteli and Mancarella [72] 2017 Theoretical/Application Behavioral risk

5. Conclusions

Both natural and man-made accidents (deliberate or accidental) can potentially damage, disable or destroy critical infrastructure. Rather than focusing on one type of threat or danger at a time, such as natural disasters or terrorist attacks, States should identify all the threats and risks that pose the greatest risks to critical infrastructure. This is the only way to think about more effective and efficient planning and allocation of resources.

This entry is adapted from the peer-reviewed paper 10.3390/su14042233

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